| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical-imaging |
| - ultrasound |
| - fetal-imaging |
| - segmentation |
| - miccai-2024 |
| pretty_name: ACOUSLIC-AI |
| size_categories: |
| - n<1K |
| --- |
| |
| # ACOUSLIC-AI Dataset |
|
|
| ## Dataset Description |
|
|
| ACOUSLIC-AI (Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries) is a dataset for the MICCAI 2024 challenge focused on fetal abdominal circumference measurement from ultrasound images. |
|
|
| ### Dataset Summary |
|
|
| - **Source**: [Zenodo](https://zenodo.org/records/12697994) |
| - **Challenge**: [ACOUSLIC-AI Grand Challenge](https://acouslic-ai.grand-challenge.org/) |
| - **Version**: 1.1 |
| - **License**: CC-BY-NC-SA 4.0 |
|
|
| ### Supported Tasks |
|
|
| - Fetal abdomen segmentation |
| - Abdominal circumference measurement |
| - Ultrasound image analysis |
|
|
| ## Dataset Structure |
|
|
| ``` |
| . |
| ├── images/ |
| │ └── stacked_fetal_ultrasound/ |
| │ └── *.mha (300 files, ~335MB each) |
| ├── masks/ |
| │ └── stacked_fetal_abdomen/ |
| │ └── *.mha (300 files, ~335MB each) |
| └── circumferences/ |
| └── fetal_abdominal_circumferences_per_sweep.csv |
| ``` |
|
|
| ### Data Fields |
|
|
| **Images (.mha)**: |
| - 3D volumetric ultrasound stacks |
| - Format: MetaImage (.mha) |
| - Each file: ~335MB |
|
|
| **Masks (.mha)**: |
| - Segmentation masks with pixel values: |
| - 0: Background |
| - 1: Optimal plane (fetal abdomen) |
| - 2: Suboptimal plane |
|
|
| **Circumferences (CSV)**: |
| - `uuid`: Unique identifier matching image/mask filenames |
| - `subject_id`: Subject identifier |
| - `sweep_1_ac_mm` to `sweep_6_ac_mm`: Abdominal circumference measurements in mm |
|
|
| ## Dataset Statistics |
|
|
| | Type | Count | Format | Size | |
| |------|-------|--------|------| |
| | Images | 300 | .mha | ~335MB each | |
| | Masks | 300 | .mha | ~335MB each | |
| | CSV | 1 | .csv | 24KB | |
|
|
| **Total size**: ~200GB |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import SimpleITK as sitk |
| |
| # Download dataset |
| local_dir = snapshot_download( |
| repo_id="Angelou0516/ACOUSLIC-AI", |
| repo_type="dataset" |
| ) |
| |
| # Load an image |
| image_path = f"{local_dir}/images/stacked_fetal_ultrasound/0199616b-bdeb-4119-97a3-a5a3571bd641.mha" |
| image = sitk.ReadImage(image_path) |
| array = sitk.GetArrayFromImage(image) # (D, H, W) numpy array |
| |
| # Load corresponding mask |
| mask_path = f"{local_dir}/masks/stacked_fetal_abdomen/0199616b-bdeb-4119-97a3-a5a3571bd641.mha" |
| mask = sitk.ReadImage(mask_path) |
| mask_array = sitk.GetArrayFromImage(mask) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original source: |
|
|
| ```bibtex |
| @misc{acouslic_ai_2024, |
| title={ACOUSLIC-AI: Abdominal Circumference Operator-agnostic UltraSound measurement in Low-Income Countries}, |
| year={2024}, |
| publisher={Zenodo}, |
| doi={10.5281/zenodo.12697994}, |
| url={https://zenodo.org/records/12697994} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is licensed under [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
|
|
| ## Acknowledgements |
|
|
| This dataset was created for the MICCAI 2024 ACOUSLIC-AI Challenge. |
|
|
